Hybrid neural network and case based reasoning system for Web user behavior clustering and classification
| Autor: | Rushed Kanawati, Farida Zehraoui, Sylvie Salotti |
|---|---|
| Přispěvatelé: | Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE), Laboratoire d'Informatique de Paris-Nord (LIPN), Université Paris 13 (UP13)-Institut Galilée-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Self-organizing map
Artificial neural network Computer science business.industry case based reasoning Login computer.software_genre Machine learning Session (web analytics) Hybrid neural network self-organizing map [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Web page hybrid neuro-CBR systems Sequence processing Case-based reasoning Artificial intelligence Data mining business Cluster analysis computer |
| Zdroj: | International Journal of Hybrid Intelligent Systems International Journal of Hybrid Intelligent Systems, 2010, 7 (3), pp.171--186. ⟨10.3233/HIS-2010-0112⟩ |
| DOI: | 10.3233/HIS-2010-0112⟩ |
| Popis: | International audience; In this paper we present Casep2: a hybrid neuro-symbolic system combining case-based reasoning (CBR) and artificial neural networks that aims at clustering and classifying users' behavior in an e-commerce site. A user behavior is represented by a sequence of visited web pages, in a session. Each registered behavior is associated to one of the following classes: buyer or non-buyer. Our goal is to provide a system that mines the web site access log in order to predict the class of an on-going user navigation. One major challenge to face is to provide scalable algorithms that can handle efficiently the large amount of data to learn from. Predictions should be made in real-time, during the current navigation. In addition, raw data has a sequential nature and are very noisy. In the proposed system, two original neural networks, named M-SOM-ART networks, are applied: one to implement the retrieval phase of a CBR cycle, and the second to implement the reuse phase. This hybrid scheme allows to ensure incremental learning as well as efficient treatment of large-scale sequential data. Experiments on real log data of an e-commerce site show the relevancy of the proposed approach. |
| Databáze: | OpenAIRE |
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